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Study on the Control Method of Temperature and Humidity Environment in Building Intelligent System

  • Kuan HuangEmail author
  • Haolin Song
  • Hongrui Fu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

Due to the difficulty of establishing the accurate control model for building an intelligent system, a neural network predictive control method is proposed, in this paper, based on a weed optimization algorithm. Through considering indoor temperature and relative humidity environment factors, a control model of temperature and humidity environment is first established in an intelligent building. Then, the hidden layer nodes center of the RBF neural network is optimized by using the weed optimization algorithm. The above mentioned work focuses on improving the shortcomings of Orthogonal Least Squares (OLS) algorithm, and simultaneously simplifies the network architecture. The simulation results show that the RBF neural network predictive control method based on the weed optimization algorithm has better approximation ability and generalization ability contrasting with the OLS algorithm.

Keywords

Radial basis function Weed optimization algorithm Neural network node centers Building intelligent system 

Notes

Acknowledgements

This research work is partially supported by the National Natural Youth Science Foundation of China (Project Codes: 61305125), Shenyang Jianzhu University Discipline Content Education Project (Project Codes: XKHY2-66), the Natural Science Foundation of University (Project Codes: 2014068) and National Post Doctor Foundation (Project Codes: 2013M530955, 2014T70265).

References

  1. 1.
    Yu, C.G., Wang, J.P., Ying, Y.B.: Greenhouse temperature prediction model based radial basis function neural networks. J. Biomath. 21(4), 549–553 (2006) (in Chinese)Google Scholar
  2. 2.
    Sherstinsky, A., Picard, R.W.: On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Trans. Neural Netw. 7(1), 195–200 (1996)CrossRefGoogle Scholar
  3. 3.
    Zhang, Z.Z., Qiao, J.F.: Design RBF neural network architecture based on online subtractive clustering. Control Decision 27(7), 997–1002 (2012) (in Chinese)Google Scholar
  4. 4.
    Ding, T., Zhou, H.C.: Prediction method research based on radial basis function neural network. J. Harbin Inst. Technol. 37(2), 272–275 (2005) (in Chinese)Google Scholar
  5. 5.
    Wang, J.S., Gao, Z.N.: Traffic modeling and prediction based on RBF neural network. Comput. Eng. Appl. 44(13), 6–11 (2008) (in Chinese)Google Scholar
  6. 6.
    He, F., Ma C.W.: Application of BP neural network based on genetic algorithm in predicting the air humidity of sunlight greenhouse. Chinese Agric. Sci. Bulletin 24(1), 492–495 (2008) (in Chinese)Google Scholar
  7. 7.
    Lin, M.Q., Chen, Z.Q., Yuan, Z.Z.: Self-tuning controller for neural network predictive deviation compensation based on damped least square. Inf. Control 29(1), 27–33 (2000) (in Chinese)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Information and Control EngineeringShenyang Jianzhu UniversityShenyangChina

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